OpenRaiser/Intern-Atlas huggingface dataset:
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their environments. However, traditional RL frameworks often face challenges in terms of iteration efficiency and safety. Risk-sensitive policy gradient methods, which incorporate both expected return and risk measures, have been explored for their ability to yield safe policies, yet their iteration complexity remains largely underexplored. In this work, we conduct a rigorous iteration complexity analysis for the risk-sensitive policy gradient method, focusing on the REINFORCE algorithm with an exponential utility function. We establish an iteration complexity of $\mathcal{O}(ε^{-2})$ to reach an $ε$-approximate first-order stationary point (FOSP). Furthermore, we investigate whether risk-sensitive algorithms can achieve better iteration complexity compared to their risk-neutral counterparts. Our analysis indicates that risk-sensitive REINFORCE can potentially converge faster. To validate our analysis, we empirically evaluate the learning performance and convergence efficiency of the risk-neutral and risk-sensitive REINFORCE algorithms in multiple environments: CartPole, MiniGrid, and Robot Navigation. Empirical results confirm that risk-sensitive cases can converge and stabilize faster compared to their risk-neutral counterparts. More details can be found on our website https://anonymous.4open.science/w/riskrl.
Revealing the Parallel Multilingual Learning within Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities. To test this capability, we design extensive experiments encompassing 8 typical datasets, 7 languages and 8 state-of-the-art multilingual LLMs. Experimental results show that (1) incorporating more languages help PiM surpass the conventional ICL further; (2) even combining with the translations that are inferior to baseline performance can also help. Moreover, by examining the activated neurons in LLMs, we discover a counterintuitive but interesting phenomenon. Contrary to the common thought that PiM would activate more neurons than monolingual input to leverage knowledge learned from diverse languages, PiM actually inhibits neurons and promotes more precise neuron activation especially when more languages are added. This phenomenon aligns with the neuroscience insight about synaptic pruning, which removes less used neural connections, strengthens remainders, and then enhances brain intelligence.
Bounds and extremal graphs for monitoring edge-geodetic sets in graphs
Venue: arXiv cs.DM 2025
Year: 2025

A monitoring edge-geodetic set, or simply an MEG-set, of a graph $G$ is a vertex subset $M \subseteq V(G)$ such that given any edge $e$ of $G$, $e$ lies on every shortest $u$-$v$ path of $G$, for some $u,v \in M$. The monitoring edge-geodetic number of $G$, denoted by $meg(G)$, is the minimum cardinality of such an MEG-set. This notion provides a graph theoretic model of the network monitoring problem. In this article, we compare $meg(G)$ with some other graph theoretic parameters stemming from the network monitoring problem and provide examples of graphs having prescribed values for each of these parameters. We also characterize graphs $G$ that have $V(G)$ as their minimum MEG-set, which settles an open problem due to Foucaud \textit{et al.} (CALDAM 2023), and prove that some classes of graphs fall within this characterization. We also provide a general upper bound for $meg(G)$ for sparse graphs in terms of their girth, and later refine the upper bound using the chromatic number of $G$. We examine the change in $meg(G)$ with respect to two fundamental graph operations: clique-sum and subdivisions. In both cases, we provide a lower and an upper bound of the possible amount of changes and provide (almost) tight examples.
Large Language Models and Causal Inference in Collaboration: A Survey
Venue: arXiv cs.CL 2025
Year: 2025

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables. The emergence of generative Large Language Models (LLMs) has significantly impacted various NLP domains, particularly through their advanced reasoning capabilities. This survey focuses on evaluating and improving LLMs from a causal view in the following areas: understanding and improving the LLMs' reasoning capacity, addressing fairness and safety issues in LLMs, complementing LLMs with explanations, and handling multimodality. Meanwhile, LLMs' strong reasoning capacities can in turn contribute to the field of causal inference by aiding causal relationship discovery and causal effect estimations. This review explores the interplay between causal inference frameworks and LLMs from both perspectives, emphasizing their collective potential to further the development of more advanced and equitable artificial intelligence systems.
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Distributionally robust offline reinforcement learning (RL), which seeks robust policy training against environment perturbation by modeling dynamics uncertainty, calls for function approximations when facing large state-action spaces. However, the consideration of dynamics uncertainty introduces essential nonlinearity and computational burden, posing unique challenges for analyzing and practically employing function approximation. Focusing on a basic setting where the nominal model and perturbed models are linearly parameterized, we propose minimax optimal and computationally efficient algorithms realizing function approximation and initiate the study on instance-dependent suboptimality analysis in the context of robust offline RL. Our results uncover that function approximation in robust offline RL is essentially distinct from and probably harder than that in standard offline RL. Our algorithms and theoretical results crucially depend on a novel function approximation mechanism incorporating variance information, a new procedure of suboptimality and estimation uncertainty decomposition, a quantification of the robust value function shrinkage, and a meticulously designed family of hard instances, which might be of independent interest.
ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images
Venue: arXiv cs.CV 2025
Year: 2025

Designing egocentric 3D hand pose estimation systems that can perform reliably in complex, real-world scenarios is crucial for downstream applications. Previous approaches using RGB or NIR imagery struggle in challenging conditions: RGB methods are susceptible to lighting variations and obstructions like handwear, while NIR techniques can be disrupted by sunlight or interference from other NIR-equipped devices. To address these limitations, we present ThermoHands, the first benchmark focused on thermal image-based egocentric 3D hand pose estimation, demonstrating the potential of thermal imaging to achieve robust performance under these conditions. The benchmark includes a multi-view and multi-spectral dataset collected from 28 subjects performing hand-object and hand-virtual interactions under diverse scenarios, accurately annotated with 3D hand poses through an automated process. We introduce a new baseline method, TherFormer, utilizing dual transformer modules for effective egocentric 3D hand pose estimation in thermal imagery. Our experimental results highlight TherFormer's leading performance and affirm thermal imaging's effectiveness in enabling robust 3D hand pose estimation in adverse conditions.
Formal Security Analysis of the AMD SEV-SNP Software Interface
Venue: arXiv cs.CR 2025
Year: 2025

AMD Secure Encrypted Virtualization technologies enable confidential computing by protecting virtual machines from highly privileged software such as hypervisors. In this work, we develop the first, comprehensive symbolic model of the software interface of the latest SEV iteration called SEV Secure Nested Paging (SEV-SNP). Our model covers remote attestation, key derivation, page swap and live migration. We analyze the security of the software interface of SEV-SNP and formally prove that most critical secrecy, authentication, attestation and freshness properties do indeed hold in the model. Furthermore, we find that the platform-agnostic nature of messages exchanged between SNP guests and the AMD Secure Processor firmware presents a potential weakness in the design. We show how this weakness leads to formal attacks on multiple security properties, including the partial compromise of attestation report integrity, and discuss possible impacts and mitigations.
GreedyML: A Parallel Algorithm for Maximizing Constrained Submodular Functions
Venue: arXiv cs.DC 2025
Year: 2025

We describe a parallel approximation algorithm for maximizing monotone submodular functions subject to hereditary constraints on distributed memory multiprocessors. Our work is motivated by the need to solve submodular optimization problems on massive data sets, for practical contexts such as data summarization, machine learning, and graph sparsification. Our work builds on the randomized distributed RandGreedi algorithm, proposed by Barbosa, Ene, Nguyen, and Ward (2015). This algorithm computes a distributed solution by randomly partitioning the data among all the processors and then employing \emph{a single} accumulation step in which all processors send their partial solutions to one processor. However, for large problems, the accumulation step exceeds the memory available on a processor, and the processor that performs the accumulation becomes a computational bottleneck. Hence we propose a generalization of the RandGreedi algorithm that employs multiple accumulation steps to reduce the memory required. We analyze the approximation ratio and the time complexity of the algorithm (in the BSP model). We evaluate the new GreedyML algorithm on three classes of problems, and report results from large-scale data sets with millions of elements. The results show that the GreedyML algorithm can solve problems where the sequential Greedy and distributed RandGreedi algorithms fail due to memory constraints. For certain computationally intensive problems, the GreedyML algorithm is faster than the RandGreedi algorithm. The observed approximation quality of the solutions computed by the GreedyML algorithm closely matches those obtained by the RandGreedi algorithm on these problems.
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Enhancing the robustness of deep learning models, particularly in the realm of vision transformers (ViTs), is crucial for their real-world deployment. In this work, we provide a finetuning approach to enhance the robustness of vision transformers inspired by the concept of nullspace from linear algebra. Our investigation centers on whether a vision transformer can exhibit resilience to input variations akin to the nullspace property in linear mappings, which would imply that perturbations sampled from this nullspace do not influence the model's output when added to the input. We start from the observation that many existing ViTs satisfy this property because their patch embedding layer has a non-trivial nullspace. Then, we extend the notion of nullspace to nonlinear settings and demonstrate that it is possible to synthesize approximate nullspace elements for ViT's encoder blocks through optimization. Finally, we propose a finetuning strategy for ViTs wherein we augment the training data with synthesized approximate nullspace noise. We find that our finetuning approach significantly improves the models' robustness to both adversarial and natural image perturbations.\footnote{Code is available at: https://github.com/Liu-Hy/ns-vit.
Moodle Usability Assessment Methodology using the Universal Design for Learning perspective
Venue: arXiv cs.CY 2025
Year: 2025

The application of the Universal Design for Learning framework favors the creation of virtual educational environments for all. It requires developing accessible content, having a usable platform, and the use of flexible didactics and evaluations that promote constant student motivation. The present study aims to design a methodology to evaluate the usability of the Moodle platform based on the principles of Universal Design for Learning, recognizing the importance of accessibility, usability and the availability of Assistive Technologies. We developed and applied a methodology to assess the usability level of Moodle platforms, taking into consideration that they integrate Assistive Technologies or are used for MOOC contexts. We provide the results of a use case that assesses two instances for the respective Moodle v.2.x and v.3.x family versions. We employed the framework of mixed design research in order to assess a MOOC-type educational program devised under the principles of Universal Design for Learning. As a result of the assessment of Moodle v.2.x and v.3.x, we conclude that the platforms must improve some key elements (e.g. contrasting colors, incorporation of alternative text and links) in order to comply with international accessibility standards. With respect to usability, we can confirm that the principles and guidelines of Universal Design for Learning are applicable to MOOC-type Virtual Learning Environments, are positively valued by students, and have a positive impact on certification rates.
Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products
Venue: arXiv cs.LG 2025
Year: 2025

Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that aim to create improved precipitation datasets by merging remote sensing and gauge data. However, ensemble learning of quantile regression algorithms remains unexplored in this context and, at the same time, it has not been substantially developed so far in the broader machine learning research landscape. Here, we introduce nine quantile-based ensemble learners and address the aforementioned gap in precipitation dataset creation by presenting the first application of these learners to large precipitation datasets. We employed a novel feature engineering strategy, which reduces the number of predictors by using distance-weighted satellite precipitation at relevant locations, combined with location elevation. Our ensemble learners include six that are based on stacking ideas and three simple methods (mean, median, best combiner). Each of them combines the following six individual algorithms: quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). These algorithms serve as both base learners and combiners within different ensemble learning methods. We evaluated performance against a reference method (i.e., QR) using quantile scoring functions and a large dataset. The latter comprises 15 years of monthly gauge-measured and satellite precipitation in the contiguous United States (CONUS). Ensemble learning with QR and QRNN yielded the best results across the various investigated quantile levels, which range from 0.025 to 0.975, outperforming the reference method by 3.91% to 8.95%...
A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of these techniques being developed, it is valuable to see the current state of the field to better understand what exists and what is not there yet. This article presents a study of these existing machine learning based approaches and demonstrates what type of representations were used for different cybersecurity tasks and programming languages. Additionally, we study what types of models are used with different representations. We have found that graph-based representations are the most popular category of representation, and tokenizers and Abstract Syntax Trees (ASTs) are the two most popular representations overall (e.g., AST and tokenizers are the representations with the highest count of papers, whereas graph-based representations is the category with the highest count of papers). We also found that the most popular cybersecurity task is vulnerability detection, and the language that is covered by the most techniques is C. Finally, we found that sequence-based models are the most popular category of models, and Support Vector Machines are the most popular model overall.
Quaternion-Based Sliding Mode Control for Six Degrees of Freedom Flight Control of Quadrotors
Venue: arXiv cs.RO 2025
Year: 2025

Despite extensive research on sliding mode control (SMC) design for quadrotors, the existing approaches suffer from certain limitations. Euler angle-based SMC formulations suffer from poor performance in high-pitch or -roll maneuvers. Quaternion-based SMC approaches have unwinding issues and complex architecture. Coordinate-free methods are slow and only almost globally stable. This paper presents a new six degrees of freedom SMC flight controller to address the above limitations. We use a cascaded architecture with a position controller in the outer loop and a quaternion-based attitude controller in the inner loop. The position controller generates the desired trajectory for the attitude controller using a coordinate-free approach. The quaternion-based attitude controller uses the natural characteristics of the quaternion hypersphere, featuring a simple structure while providing global stability and avoiding unwinding issues. We compare our controller with three other common control methods conducting challenging maneuvers like flip-over and high-speed trajectory tracking in the presence of model uncertainties and disturbances. Our controller consistently outperforms the benchmark approaches with less control effort and actuator saturation, offering highly effective and efficient flight control.
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Anomaly detection is vital in various industrial scenarios, including the identification of unusual patterns in production lines and the detection of manufacturing defects for quality control. Existing techniques tend to be specialized in individual scenarios and lack generalization capacities. In this study, our objective is to develop a generic anomaly detection model that can be applied in multiple scenarios. To achieve this, we custom-build generic visual language foundation models that possess extensive knowledge and robust reasoning abilities as anomaly detectors and reasoners. Specifically, we introduce a multi-modal prompting strategy that incorporates domain knowledge from experts as conditions to guide the models. Our approach considers diverse prompt types, including task descriptions, class context, normality rules, and reference images. In addition, we unify the input representation of multi-modality into a 2D image format, enabling multi-modal anomaly detection and reasoning. Our preliminary studies demonstrate that combining visual and language prompts as conditions for customizing the models enhances anomaly detection performance. The customized models showcase the ability to detect anomalies across different data modalities such as images, point clouds, and videos. Qualitative case studies further highlight the anomaly detection and reasoning capabilities, particularly for multi-object scenes and temporal data. Our code is publicly available at https://github.com/Xiaohao-Xu/Customizable-VLM
Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF
Venue: arXiv cs.RO 2025
Year: 2025

The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency and ensure robot safety, we propose the Risk-aware Environment Masking (RaEM) framework. RaEM leverages coherent risk measures to dynamically prioritize safety-critical regions of the unknown environment, guiding active view acquisition algorithms toward identifying the next-best-view (NBV). Integrated with FisherRF, which selects the NBV by maximizing expected information gain, our framework achieves a dual objective: improving robot safety and increasing efficiency in risk-aware 3D scene reconstruction and understanding. Extensive high-fidelity experiments validate the effectiveness of our approach, demonstrating its ability to establish a robust and safety-focused framework for active robot exploration and 3D scene understanding.
Accelerating Handover in Mobile Satellite Network
Venue: arXiv cs.NI 2025
Year: 2025

The construction of Low Earth Orbit (LEO) satellite constellations has recently spurred tremendous attention from academia and industry. 5G and 6G standards have specified LEO satellite network as a key component of 5G and 6G networks. However, ground terminals experience frequent, high-latency handover incurred by satellites' fast travelling speed, which deteriorates the performance of latency-sensitive applications. To address this challenge, we propose a novel handover flowchart for mobile satellite networks, which can considerably reduce the handover latency. The innovation behind this scheme is to mitigate the interaction between the access and core networks that occupy the majority of time overhead by leveraging the predictable travelling trajectory and spatial distribution inherent in mobile satellite networks. Specifically, we design a fine-grained synchronized algorithm to address the synchronization problem due to the lack of control signalling delivery between the access and core networks. Moreover, we minimize the computational complexity of the core network using information such as the satellite access strategy and unique spatial distribution, which is caused by frequent prediction operations. We have built a prototype for a mobile satellite network using modified Open5GS and UERANSIM, which is driven by actual LEO satellite constellations such as Starlink and Kuiper. We have conducted extensive experiments, and the results demonstrate that our proposed handover scheme can considerably reduce the handover latency compared to the 3GPP Non-terrestrial Networks (NTN) and two other existing handover schemes.